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Asynchronous I/O Persistence for In-Memory database Servers: Leveraging io_uring to Optimize Redis Persistence 17th

Asynchronous I/O Persistence for In-Memory Database Servers:...

17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Le-Gao Li, Yanzhi Laohakangvalvit, Tipporn Sugaya, Midori Innovative Global Program College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan Department of Computer Science and Engineering College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan

ISBN: (纸本)9783031771521

With constant hardware improvements allowing for increasingly large memory sizes, in-memory database servers have become an attractive option for various cloud applications. Even though in-memory database servers store all data in memory, there is still a need to persist data onto an I/O device for maintenance, updates, or protection against server crashes. In high throughput environments, persistence happens often, significantly hindering server throughput performance. Although popular in-memory database servers such as Redis or Dragonfly DB have adopted asynchronous I/O for socket operations, none have extended this approach for persistence. This results in significantly longer time taken for persistence, leading to prolonged reduced throughput. Given the recent introduction of io_uring, which has demonstrated significant performance improvements over past asynchronous I/O APIs, we have decided to implement it into Redis to evaluate its effectiveness in reducing these performance overheads. Our findings indicate that io_uring can significantly enhance performance in use cases with medium to large payload sizes but may hinder performances in use cases involving small payload sizes due to higher complexity and higher operation cost per datapoint. This method can likely be applied to other servers, such as Dragonfly DB and Memcached, to achieve similar benefits in appropriate scenarios. Additionally, in-memory database server persistence can serve as valuable benchmarks for further io_uring development and optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking

arXiv

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arXiv 2025年

作者： Wang, Ruilin Feng, Xiang Yu, Huiqun Lai, Edmund M.-K. Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Department of Data Science and Artificial Intelligence Auckland University of Technology Auckland New Zealand

In evolutionary multitasking, strategies such as crossover operators and skill factor assignment are critical for effective knowledge transfer. Existing improvements to crossover operators primarily focus on low-dimensional variable combinations, such as arithmetic crossover or partially mapped crossover, which are insufficient for modeling complex high-dimensional ***, static or semi-dynamic crossover strategies fail to adapt to the dynamic dependencies among tasks. In addition, current Multifactorial Evolutionary Algorithm frameworks often rely on fixed skill factor assignment strategies, lacking flexibility. To address these limitations, this paper proposes the Multifactorial Evolutionary Algorithm-Residual Learning (MFEA-RL) method based on residual learning. The method employs a Very Deep Super-Resolution (VDSR) model to generate high-dimensional residual representations of individuals, enhancing the modeling of complex relationships within dimensions. A ResNet-based mechanism dynamically assigns skill factors to improve task adaptability, while a random mapping mechanism efficiently performs crossover operations and mitigates the risk of negative transfer. Theoretical analysis and experimental results show that MFEA-RL outperforms state-of-the-art multitasking algorithms. It excels in both convergence and adaptability on standard evolutionary multitasking benchmarks, including CEC2017-MTSO and WCCI2020-MTSO. Additionally, its effectiveness is validated through a real-world application scenario. Copyright © 2025, The Authors. All rights reserved.

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A survey on machine learning approaches for vital sign monitoring using radar

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Shirazi, Mohammad Hossein Yongchareon, Sira Singh, Anuradha Ma, Jing Department of Data Science and AI School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

The integration of machine learning methodologies with radar-based vital sign monitoring represents a significant advancement in non-contact healthcare surveillance systems. This systematic literature review synthesizes and critically analyzes research from 2020 to 2025, addressing substantive theoretical and methodological gaps in extant literature. Our comprehensive taxonomic classification of machine learning paradigms employed in this domain elucidates the progressive refinement from conventional algorithmic approaches to sophisticated deep learning architectures, with particular emphasis on hybrid neural network configurations optimized for physiological signal extraction in non-stationary environments. Methodologically, this survey contributes a rigorous evaluation framework comprising standardized assessment protocols, quantifiable performance metrics, and cross-validation methodologies—elements conspicuously absent in previous reviews. Empirical analysis demonstrates substantial correlations between dataset demographic characteristics and algorithmic generalizability, with heterogeneous participant cohorts yielding markedly enhanced performance across cardiac, respiratory, and hemodynamic parameter estimation tasks. The review delineates four distinct developmental phases in the field's chronological evolution and provides analytical insight into persistent technical challenges: motion artifact compensation, multi-subject disambiguation, and the translation of laboratory efficacy to clinical utility. This comprehensive examination of computational approaches for radar-based vital sign monitoring establishes a theoretical foundation and methodological framework to guide future research towards physiologically robust and clinically viable implementations. © 2025 The Authors

关键词： Brain

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EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zheng, Bowen Cheng, Ran Tan, Kay Chen Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutionary Computation (EC) paradigm with RL. However, the population-based nature of EC significantly increases computational costs, thereby restricting the exploration of algorithmic design choices and scalability in large-scale settings. To address this challenge, we introduce EvoRL1, the first end-to-end EvoRL framework optimized for GPU acceleration. The framework executes the entire training pipeline on accelerators, including environment simulations and EC processes, leveraging hierarchical parallelism through vectorization and compilation techniques to achieve superior speed and scalability. This design enables the efficient training of large populations on a single machine. In addition to its performance-oriented design, EvoRL offers a comprehensive platform for EvoRL research, encompassing implementations of traditional RL algorithms (e.g., A2C, PPO, DDPG, TD3, SAC), Evolutionary Algorithms (e.g., CMA-ES, OpenES, ARS), and hybrid EvoRL paradigms such as Evolutionary-guided RL (e.g., ERL, CEM-RL) and Population-Based AutoRL (e.g., PBT). The framework’s modular architecture and user-friendly interface allow researchers to seamlessly integrate new components, customize algorithms, and conduct fair benchmarking and ablation studies. The project is open-source and available at: https://***/EMI-Group/evorl. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Deep Support Vein Machine for Lung Parcellation

Deep Support Vein Machine for Lung Parcellation

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Peng, Haichao Fang, Hao Fan, Wenkang Wang, Yong Ke, Sunkui Luo, Jie Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China Massachusetts General Hospital Harvard Medical School Boston02114 United States

ISBN: (纸本)9798350368741

Pulmonary segments parcellation is essential to thoracoscopic segmentectomy. Surgeons manually outline pulmonary segments from preoperative images before surgery, which is a time-consuming, labor-intensive and mental-stress procedure. This work proposes a novel small learning model of deep support vein machine without using annotated pulmonary segments data for automatic lung parcellation. Specifically, this machine can learn anatomical structures of pulmonary lobe, bronchus, artery, and vein by two cascade multilayer perceptrons to automatically divides the lung into eighteen segments. The perceptron module typically smooths the boundary of the pulmonary segment to attain robust and precise parcellation. Additionally, three new metrics are defined to quantitatively evaluate the quality of lung parcellation. We validate our methods on 108 clinical pulmonary computed tomography scans, with the experimental results showing that our proposed machine certainly outperforms current methods and provides a promising way to fully automated lung parcellation. Particularly, the dice similarity coefficient of lung parcellation was significantly improved from 0.886 to 0.918. © 2025 IEEE.

关键词： Lung Segmentectomy Multilayer Perceptron Pulmonary Segments Video-Assisted Thoracoscopic Surgery

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Virtual staining for pathology: Challenges, limitations and perspectives

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Intelligent Oncology 2025年第2期1卷 105-119页

作者： Weiping Lin Yihuang Hu Runchen Zhu Baoshun Wang Liansheng Wang Department of Computer Science School of Informatics Xiamen University Xiamen Fujian 361005 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen Fujian 361005 China

In pathological examinations, tissue must first be stained to meet specific diagnostic requirements, a meticulous process demanding significant time and expertise from specialists. With advancements in deep learning, this staining process can now be achieved through computational methods known as virtual staining. This technique replicates the visual effects of traditional histological staining in pathological imaging, enhancing efficiency and reducing costs. Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios. Unlike previous reviews that broadly cover the clinical applications of virtual staining, this paper focuses on the technical methodologies, encompassing current models, datasets, and evaluation methods. It highlights the unique challenges of virtual staining compared to traditional image translation, discusses limitations in existing work, and explores future perspectives. Adopting a macro perspective, we avoid overly intricate technical details to make the content accessible to clinical experts. Additionally, we provide a brief introduction to the purpose of virtual staining from a medical standpoint, which may inspire algorithm-focused researchers. This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians, fostering the integration of technical solutions and medical expertise in the development of virtual staining models. This collaboration seeks to create more efficient, generalized, and versatile virtual staining models for a wide range of clinical applications.

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AIGC-Driven Real-Time Interactive 4D Traffic Scene Generation in Vehicular Networks

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IEEE Network 2025年

作者： Li, Xiaolong Deng, Ruiting Wei, Jianhao Wu, Xin Chen, Jiayuan Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan University of Technology and Business School of Computer Science China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Concordia University Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Real-time, interactive 4D traffic scene generation enables rapid digital twinning of traffic scenarios, improving management and decision-making in intelligent transportation systems. However, current text-to-video models, such as Sora, struggle to maintain the temporal coherence of traffic elements and interact with dynamic environments and users when generating 4D scenes. This article introduces a novel cloud-edge-terminal collaborative framework that leverages Artificial Intelligence-Generated Content (AIGC) in vehicular networks to tackle these challenges, ensuring long-term coherence and improved interactivity. The framework presents a comprehensive architecture for real-time interactive 4D scene generation, encompassing data collection, management, model pre-training, fine-tuning, and inference. We examine key design requirements and challenges, demonstrating that our microservice-based framework enables the system to generate and update 4D traffic scenes in real time, effectively responding to traffic data and user inputs. To the best of our knowledge, this is the first successful implementation of real-time, interactive 4D traffic scene generation. Performance evaluations show the superiority of our framework, powered by microservice-based code fine-tuning, over traditional frameworks. Finally, we discuss future research directions to enhance AIGC-driven 4D traffic scene generation. © 1986-2012 IEEE.

关键词： Decision making

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Node-Level Lymph Node Automatic Segmentation in CT Images Using Deep Parallel Structure-Related 3D U-Net Variant 21st

Node-Level Lymph Node Automatic Segmentation in CT Images Us...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Cheng, Shi Li, Quan Zhang, Guangyu Zhang, Lei Peng, Tao School of Future Science and Engineering Soochow University Suzhou China Center of Stomatology The Second Affiliated Hospital of Soochow University Suzhou China Division of Cardiology Department of Internal Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan China

ISBN: (纸本)9789819601240

The delineation of Lymph Nodes (LNs) is pivotal in pinpointing therapeutic targets for radiotherapy in head and neck malignancies. Nevertheless, this endeavor poses a formidable challenge, primarily stemming from the suboptimal contrast against adjacent tissues. This investigation introduces a deep learning methodology aimed at automating the segmentation of LNs within CT scans, offering the following contributions: (1) Expanding upon the 3D Unet model, we incorporate a parallel block consisting of attention gate and squeeze & excitation modules. We extensively evaluate various versions of this parallel block and achieve favorable performance. (2) To address the slow decrease in Dice loss, we introduce a lightweight boundary refinement module. Our proposed method is assessed on a dataset comprising 103 patients and 603 Lymph Nodes (LNs), with 452 nodes used for training and 151 nodes for testing. The node-level Dice similarity coefficient achieved by our method reaches an impressive 0.833. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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